Field
Embodiments of the invention relate to improving demand forecasts. More specifically, embodiments of the invention relate to combining data from independent demand forecasts to improve demand forecasting.
Background
Demand forecasting has long been an integral part of supply chain management. Advanced planner and optimizer (APO) software available form SAP AG of Walldorf, Germany includes a demand planning module, which provides a demand forecast data stream that may be used in supply network planning (SNP). The demand planning module uses statistical forecasting techniques and other methods for demand forecasting. The demand planning module may generate a forecast of products sold and generate a sales plan of the scenario, which serves as a starting point for subsequent planning activities. The demand plan is commonly based on historical data, which may be available directly from enterprise resource planning (ERP) systems or some other archival system. The demand planning module may use various common forecasting methods such as, smooth average values, trend models, multiple linear regression analysis, etc. Typically, the demand planning module provides relatively low granularity using time intervals of one week or possibly an interval of a month or longer in creating its demand plan. The demand plan is then released to the supply network planning module of the APO, which uses the demand plan to determine production procurement and distribution schedules at a tactical level.
With the expansion of vendor management inventory (VMI), responsive replenishment (RR) systems provide order forecasts typically based on a daily time interval. RR often provides a very accurate short-term outlook with accuracy deteriorating with increase time into the future. Use of the demand planning data stream often yields significant demand inaccuracies in the demand forecast due in part to its low granularity. However, replacing demand planning forecasts with the responsive replenishment forecast also tends to yield an inaccurate ultimate demand forecast.